{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "#只在训练集train.csv和测试集test.cv出现的活动\n",
    "PE_event = pd.read_csv('PE_event.csv')\n",
    "# train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13418, 111)"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PE_event.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>event_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>start_time</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip</th>\n",
       "      <th>country</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>...</th>\n",
       "      <th>c_92</th>\n",
       "      <th>c_93</th>\n",
       "      <th>c_94</th>\n",
       "      <th>c_95</th>\n",
       "      <th>c_96</th>\n",
       "      <th>c_97</th>\n",
       "      <th>c_98</th>\n",
       "      <th>c_99</th>\n",
       "      <th>c_100</th>\n",
       "      <th>c_other</th>\n",
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       "  </thead>\n",
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       "      <td>3928440935</td>\n",
       "      <td>517514445</td>\n",
       "      <td>2012-11-05T00:00:00.001Z</td>\n",
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       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2582345152</td>\n",
       "      <td>781585781</td>\n",
       "      <td>2012-10-30T00:00:00.001Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
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       "      <td>1051165850</td>\n",
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       "      <td>2012-09-27T00:00:00.001Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 111 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    event_id     user_id                start_time city state  \\\n",
       "0           0   684921758  3647864012  2012-10-31T00:00:00.001Z  NaN   NaN   \n",
       "1           1   244999119  3476440521  2012-11-03T00:00:00.001Z  NaN   NaN   \n",
       "2           2  3928440935   517514445  2012-11-05T00:00:00.001Z  NaN   NaN   \n",
       "3           3  2582345152   781585781  2012-10-30T00:00:00.001Z  NaN   NaN   \n",
       "4           4  1051165850  1016098580  2012-09-27T00:00:00.001Z  NaN   NaN   \n",
       "\n",
       "   zip country  lat  lng   ...     c_92  c_93  c_94  c_95  c_96  c_97  c_98  \\\n",
       "0  NaN     NaN  NaN  NaN   ...        0     1     0     0     0     0     0   \n",
       "1  NaN     NaN  NaN  NaN   ...        0     0     0     0     0     0     0   \n",
       "2  NaN     NaN  NaN  NaN   ...        0     0     0     0     0     0     0   \n",
       "3  NaN     NaN  NaN  NaN   ...        0     0     0     0     0     0     0   \n",
       "4  NaN     NaN  NaN  NaN   ...        0     0     0     0     0     0     0   \n",
       "\n",
       "   c_99  c_100  c_other  \n",
       "0     0      0        9  \n",
       "1     0      0        7  \n",
       "2     0      0       12  \n",
       "3     0      0        8  \n",
       "4     0      0        9  \n",
       "\n",
       "[5 rows x 111 columns]"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PE_event.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        2012-10-31T00:00:00.001Z\n",
      "1        2012-11-03T00:00:00.001Z\n",
      "2        2012-11-05T00:00:00.001Z\n",
      "3        2012-10-30T00:00:00.001Z\n",
      "4        2012-09-27T00:00:00.001Z\n",
      "5        2012-11-16T00:00:00.001Z\n",
      "6        2012-11-02T20:00:00.003Z\n",
      "7        2012-10-31T00:00:00.001Z\n",
      "8        2012-10-31T00:00:00.001Z\n",
      "9        2012-10-18T00:00:00.001Z\n",
      "10       2012-11-06T22:40:00.003Z\n",
      "11       2012-08-31T00:00:00.001Z\n",
      "12       2012-11-12T17:00:00.003Z\n",
      "13       2012-12-01T00:00:00.001Z\n",
      "14       2012-08-26T17:00:00.003Z\n",
      "15       2012-08-31T00:00:00.001Z\n",
      "16       2012-10-31T00:00:00.001Z\n",
      "17       2012-11-08T00:00:00.001Z\n",
      "18       2012-11-16T20:00:00.002Z\n",
      "19       2012-09-28T00:00:00.001Z\n",
      "20       2012-09-30T00:00:00.001Z\n",
      "21       2012-10-23T00:00:00.001Z\n",
      "22       2012-11-08T00:00:00.001Z\n",
      "23       2012-10-05T00:00:00.001Z\n",
      "24       2012-10-04T00:00:00.001Z\n",
      "25       2012-10-31T00:00:00.001Z\n",
      "26       2012-09-27T02:00:00.003Z\n",
      "27       2012-11-04T00:00:00.001Z\n",
      "28       2012-10-31T00:00:00.001Z\n",
      "29       2012-09-30T00:00:00.001Z\n",
      "                   ...           \n",
      "13388    2012-10-31T04:00:00.003Z\n",
      "13389    2012-11-15T03:00:00.003Z\n",
      "13390    2012-11-03T04:00:00.003Z\n",
      "13391    2012-11-01T10:00:00.002Z\n",
      "13392    2012-10-27T01:00:00.003Z\n",
      "13393    2012-10-02T18:00:00.003Z\n",
      "13394    2012-10-31T02:00:00.003Z\n",
      "13395    2012-11-11T00:00:00.001Z\n",
      "13396    2012-10-30T00:00:00.001Z\n",
      "13397    2012-10-07T04:30:00.003Z\n",
      "13398    2012-11-22T01:00:00.003Z\n",
      "13399    2012-12-15T13:30:00.003Z\n",
      "13400    2012-10-15T00:00:00.003Z\n",
      "13401    2012-11-01T00:00:00.003Z\n",
      "13402    2012-11-10T12:01:00.003Z\n",
      "13403    2012-10-28T02:00:00.003Z\n",
      "13404    2012-11-09T14:00:00.003Z\n",
      "13405    2012-10-27T01:30:00.003Z\n",
      "13406    2012-11-03T15:00:00.003Z\n",
      "13407    2012-11-14T01:00:00.003Z\n",
      "13408    2012-10-30T00:00:00.001Z\n",
      "13409    2012-07-21T19:00:00.000Z\n",
      "13410    2012-07-19T17:30:00.000Z\n",
      "13411    2012-10-29T01:00:00.003Z\n",
      "13412    2012-09-20T00:00:00.001Z\n",
      "13413    2012-10-30T07:00:00.003Z\n",
      "13414    2012-11-10T03:00:00.003Z\n",
      "13415    2012-09-28T21:00:00.003Z\n",
      "13416    2012-10-31T00:00:00.001Z\n",
      "13417    2012-10-26T03:00:00.003Z\n",
      "Name: start_time, Length: 13418, dtype: object\n",
      "0        1.351613e+09\n",
      "1        1.351872e+09\n",
      "2        1.352045e+09\n",
      "3        1.351526e+09\n",
      "4        1.348675e+09\n",
      "5        1.352995e+09\n",
      "6        1.351858e+09\n",
      "7        1.351613e+09\n",
      "8        1.351613e+09\n",
      "9        1.350490e+09\n",
      "10       1.352213e+09\n",
      "11       1.346342e+09\n",
      "12       1.352711e+09\n",
      "13       1.354291e+09\n",
      "14       1.345972e+09\n",
      "15       1.346342e+09\n",
      "16       1.351613e+09\n",
      "17       1.352304e+09\n",
      "18       1.353067e+09\n",
      "19       1.348762e+09\n",
      "20       1.348934e+09\n",
      "21       1.350922e+09\n",
      "22       1.352304e+09\n",
      "23       1.349366e+09\n",
      "24       1.349280e+09\n",
      "25       1.351613e+09\n",
      "26       1.348682e+09\n",
      "27       1.351958e+09\n",
      "28       1.351613e+09\n",
      "29       1.348934e+09\n",
      "             ...     \n",
      "13388    1.351627e+09\n",
      "13389    1.352920e+09\n",
      "13390    1.351886e+09\n",
      "13391    1.351735e+09\n",
      "13392    1.351271e+09\n",
      "13393    1.349172e+09\n",
      "13394    1.351620e+09\n",
      "13395    1.352563e+09\n",
      "13396    1.351526e+09\n",
      "13397    1.349555e+09\n",
      "13398    1.353517e+09\n",
      "13399    1.355549e+09\n",
      "13400    1.350230e+09\n",
      "13401    1.351699e+09\n",
      "13402    1.352520e+09\n",
      "13403    1.351361e+09\n",
      "13404    1.352441e+09\n",
      "13405    1.351273e+09\n",
      "13406    1.351926e+09\n",
      "13407    1.352826e+09\n",
      "13408    1.351526e+09\n",
      "13409    1.342868e+09\n",
      "13410    1.342690e+09\n",
      "13411    1.351444e+09\n",
      "13412    1.348070e+09\n",
      "13413    1.351552e+09\n",
      "13414    1.352488e+09\n",
      "13415    1.348837e+09\n",
      "13416    1.351613e+09\n",
      "13417    1.351192e+09\n",
      "Name: start_time, Length: 13418, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import time \n",
    "import datetime\n",
    "#UTC时间字符串转时间timestamp2\n",
    "def dataStrToTime(dataStr):\n",
    "    return time.mktime(datetime.datetime.strptime(str(dataStr)[:19],'%Y-%m-%dT%H:%M:%S').timetuple())\n",
    "print PE_event['start_time']\n",
    "PE_event['start_time'] = PE_event['start_time'].apply(lambda x:dataStrToTime(x))\n",
    "print PE_event['start_time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13418, 102)"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#处理掉不要的特征\n",
    "PE_event = PE_event.drop(['Unnamed: 0','city','state','zip','country','lat','lng','user_id','event_id'],axis=1)\n",
    "PE_event.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00863861, 0.00088661, 0.        , ..., 0.        , 0.        ,\n",
       "        0.00062178],\n",
       "       [0.00864026, 0.00088661, 0.        , ..., 0.        , 0.        ,\n",
       "        0.00048361],\n",
       "       [0.00864137, 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.00082905],\n",
       "       ...,\n",
       "       [0.00862087, 0.        , 0.00261414, ..., 0.        , 0.        ,\n",
       "        0.00069087],\n",
       "       [0.00863861, 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.00041452],\n",
       "       [0.00863591, 0.00265983, 0.00261414, ..., 0.        , 0.        ,\n",
       "        0.0091195 ]])"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "PE_event = preprocessing.normalize(PE_event, norm='l2',axis=0,copy=False)\n",
    "PE_event\n",
    "# PE_event = min_max_scaler.fit_transform(PE_event)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "260.1645789798117 10 0.05301768639511424\n",
      "168.33021892854444 20 -0.04577750829042681\n",
      "116.17114838418455 30 -0.07322816414611119\n",
      "93.77157149209732 40 -0.17957135181674302\n",
      "82.63314514571596 50 -0.10451515010490885\n",
      "76.80186359277945 60 -0.09196628663494844\n",
      "63.867856315166236 80 -0.10386783247726783\n",
      "56.90570091162677 100 -0.21560291655534797\n"
     ]
    },
    {
     "data": {
      "image/png": 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a6gY21DVytCXyf0NOVsZJ3VVnlRaSnxP235jSHb3inEQiKCREEu9Icyub6g+c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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b41fb3fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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m/PmK3izaWMwvX1sakV/ajQQKFYkISfGxXNM/k5lfFbJxe/i+cPbWl1uYs3ob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\nAZR1caVMSzOBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b41ec9c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import MiniBatchKMeans\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "#用MiniBatchKMeans聚合，且用CH_scores评估结果\n",
    "def MiniBatchKMeansByCH_scores(n_clusters_arrary,event):\n",
    "    CH_scores = []\n",
    "    SH_scores = []\n",
    "    for n_clusters in n_clusters_arrary:\n",
    "        mb_kmeans = MiniBatchKMeans(n_clusters = n_clusters,init='k-means++')\n",
    "        mb_kmeans.fit(event)\n",
    "        CH_score = metrics.calinski_harabaz_score(event,mb_kmeans.predict(event))\n",
    "        SH_score = metrics.silhouette_score(event,mb_kmeans.predict(event)) \n",
    "        print CH_score,n_clusters,SH_score\n",
    "        CH_scores.append(CH_score)\n",
    "        SH_scores.append(SH_score)\n",
    "    plt.plot(n_clusters_arrary, np.array(CH_scores))\n",
    "    plt.xlabel('n_clusters');\n",
    "    plt.ylabel('CH_scores');\n",
    "    plt.show()   \n",
    "    plt.plot(n_clusters_arrary, np.array(SH_scores))\n",
    "    plt.xlabel('n_clusters');\n",
    "    plt.ylabel('SH_score');\n",
    "    plt.show() \n",
    "n_clusters_arrary = [10,20,30,40,50,60,80,100]\n",
    "MiniBatchKMeansByCH_scores(n_clusters_arrary,PE_event)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1428.3793113380552 2 0.5133406939222248\n",
      "972.3959355294796 3 0.48344080440016207\n",
      "690.2180363821201 4 0.18457510603525457\n",
      "585.5049104610915 5 0.3116415723175182\n",
      "451.5370520060338 6 0.05949344815898326\n",
      "410.0732386388166 7 0.08547016763692795\n",
      "351.11053306342995 8 0.07002128358580484\n",
      "295.4324419271699 9 0.14755091287382244\n",
      "320.97361173213886 10 0.13099367870227024\n",
      "241.42431023290442 11 0.06571132819342773\n",
      "235.55302647067978 12 0.10524430691093593\n",
      "235.41148469966825 13 0.06432881356650509\n",
      "263.54376922341896 14 0.05218645238587515\n",
      "231.86981571376964 15 0.05612533867602172\n",
      "239.08101714513612 16 0.07293314794802928\n",
      "274.04236019710805 17 -0.10115395353074626\n",
      "172.55706233283013 18 0.1502745749746872\n",
      "170.80309495094653 19 0.10012187235023576\n"
     ]
    },
    {
     "data": {
      "image/png": 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Zuy1y7/luHTMZOaA7IwtzKczPoXhFBdOWljN/zTbcI1fkn3lcHmMG53P24Hx6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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b4a4e3e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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p16zcFFDtV8Fge2UjXd2GgvG+nybdV6xu48AYswZY02ff3b2eb8ZRhaUskp+T\nSGeXYeeRRs4YP2ZIxyipauKRdft5dXsVIQJj46N4Y+dRAMYnx7BwaioLp6VyzuRkj5er7e42/ODF\nbbR1dvO7a/NH9FrNPaalxbPimwXc9NRmvvd8MY9dX0hY6MDf44wx/GnTIX7+WglxkWE8fkMhF+d6\nPqgxJiKMXy6bw4Ipyfzk5R1c+pv1PLBsLktmpQ/1n6M80DMuKpDvOCxPHGrk6+n5UVze4FHiMMbw\nj4PH+cO6/by/p5bYiFCWnzuBm8+bRJotkrJjrazfW8v6vbWsLqrk2U2HCA8VCnKSWDgtlQumpZKX\nYRt0bqlHPzjAhtJj3H/lbCYPMMvoSLNwWir3Lp3JT17ewc9fK+Gey2f2W7a2uZ0frd7Ou7truGBa\nqlemT1k6L4t52Yl87/libv3TVq4/Zzz/cVmuZWNeRovi8gYmpsSSFDu0gX8jgSYONaix8VGMS4p2\newR5d7fhrZJqHlm3n+LyBpJjI/jhJdP41vwJJMR8tk73xJRYJqbEcsOCCbTbu9h6qJ51e2tZv7eO\nB9fu4cG1e0iJi+C8KSksnJbK+VNTT1t/eXtlAw+u3cOls9L5+pnZfUMZ8b559njK6lp49IODTEiO\n4UYXM/e+t7vm1GDGe76Sxw0LJnitt9j45FheunUBD7yxm8c+PMjmsnp+/438gErAgcQYQ3F5PQun\njcw2OHdp4lBuKchJYvMgk/V12Lv5+7bDPLJuP/trW8geE83Pls7k6sLsQb/FRoaFsmByCgsmp/Dj\nS6GmuY0P9taxfl8t6/fV8bdtjs52eRk2Fk5LZeG0FGZmJHDHqm2kxkdy35WzR1TXW0/cdWkuZcda\nuffVXeQkx5yaU6uts4v71pTw9EeHmJEez5//ab5PBjNGhIXw0y/nsWBKMv/24id85eEPuXfpLK4q\nyArYa9qfzq5uNpcd552SGrqN4e4v5/n131hx/CR1J7wz8M9Kbq05Hmh0zXHve3LDQf77/3bx0Y8v\nOq2754l2O6v+Uc7jHx6kqrGN3Awbt14wiS/Nzhi03t4d3d2GnUeaWL+vlnV7ayk6VI+92xAijkE/\nz/3TfM6ZnDzs81iptcPO1Y98RFldC3+5dQEAd6wqZl/NCW4+byJ3Lp7ulyqko41t3LGqmI8PHuer\n+Vn87IpZHrc5jTSNrZ28v7eGt0tqWLenhqa2z2aTffNfF3p1LMVg/r7tMHes2sZrt5/HzEzvLyY2\nXO6uOR4WDHlGAAASQklEQVTYnwjlN71XBMyY7Ugcx06089TGMp756BCNJzuZP2kM9105mwumpXr1\nW1xIiDB7XAKzxyXw3Qun0NzWyUf7j/HBvjpmZMQHfNIAR4P14zecyRUrNvCtxz+muc1OQkw4zyw/\ny6/VGukJUTz37fn8/t1SfvvOXorL6/n9Nwp8smKiLx2sa+GdkmreLqlmc1k9Xd2GlLgIFs9MZ1Fe\nGtPT4rnwofd5Y8dRvyaOokP1xESEMt2P5/QFTRzKLbkZNiLDQig6VM/srAQe/eAAL26poN3ezSV5\nadx6weRTo8x9LT4qnEtmpnPJzODqBZSeEMXjNxZyzcpNXDA9lV9eNYcxFjSghoYIdyyaytmTxvD9\nVdu4YsUGZo9LID87iXk5ieRnJzIuKXpEVWPZu7opKm84lSz21zq6eU9Pi+c7CyexKC+NeeM+v9Le\nGTlJvLHjKLdf7N5CSt5QVN7A3HGJXrkTt5JWVSm3LfvDRvYcbaa1s4sQga/mZ3HLwslMGasNqd7U\n2dVN+Aj5w3K8pYNHPzjA1rJ6th9uoK3TMWAwJS6S/JxExyM7iTnjEoj1c5VWc1sn6/fW8U5JNe/t\nqaG+tZPwUOHsicksyh3LxblpZI/pf5qcxz44wM9fK2H9nReSk+z76XROdnQx+5613LJwEv++xP/T\n/LtDq6qU1104YywlVU2nutSmJ/h+Nb3RaKQkDYAxsRH8yPlHrrOrmz1Hmykur6e4vIHiigbe2lUN\nQIg4xqbk5ySRn5NIQU4ik1Lihr1Mb1e3obmtk4bWThpOdtLQ2sHBuhbe3V3DpgPH6OwyJMaEc9F0\nR6JYOC2F+KjwwQ8MLJ6Zzs9fK2HtzqN8e+GkYcXpjk8PN2LvNgHfMA56x6E8YIzBGHy+ZrcKHPUt\nHWyrbHAkkvJ6tlU00OxsfI6PCmNetqNqKz8nienp8bR1dtFwspPG1k7qWztOJYTG1g5nYvhsu761\n07kY1ennnZway6LcNC7OTaMgZ+hVP1/63QdEhYey+p8XDOcyuOWRdfu5//XdbP3pohE3j1oPveNQ\nXicijKBqbTUCJMVGcOH0sVw4fSzg6AF3oK7FcVdS4Ugov3+vlIFmcxcBW1Q4iTHhJEaHkxATwfgx\nMZ/bTowOJyk2nIToCNJskV6bqXnxzHT+9+291DS1Mdbm2zvo4vJ6xifHjNik4QlNHEoprwkJEaaM\njWPK2DiuLnQMyGxpt/Pp4UZKa04QFxlGgjMhJDoTgi063OuzxLpryax0fv3WXt7cVc11Ply7xRhD\nUXkD501J8dk5/EkTh1LKp2Ijw5g/KZn5k0Zet+mpY+OYlBLL2p1HfZo4KutPUtvcToGXFm6y2shp\nhVNKKT8TERbPSuej/cdoaO3w2XmKKxzLEviry7qvaeJQSo1qS2amY+82vFNS47NzFB2qJzo8lBk+\nmDLGCpo4lFKj2pxxCWQkfDbNvy8Ul9czZ1xCwA/86xEc/wqllBoiEWHxzHTW762ltcM++Bs81NbZ\nxc4jTUFTTQWaOJRSisUz02m3d7NuT63Xj73j1MC/4GgYB00cSinFmROSGBMb4ZPqqlMr/ukdh1JK\nBY+w0BC+mJvGuyU1tNu7vHrs4vIGssdEn7YIWSDTxKGUUjgGAza329m4/5jXjukY+FcfFPNT9aaJ\nQymlgAVTkomLDGPtDu9VVx1pbKO6qV0Th1JKBaPIsFAumjGWN3dV0zXQ5FoeKD7VvhE8DeOgiUMp\npU5ZPDOd4y0dbC477pXjFR1qICo8hNwMm1eON1JYnjhEZImI7BGRUhG5y8XrkSLygvP1j0Vkgv+j\nVEqNBl+YnkpEWAhrvdS7qqi8njlZiSNqjRVvsPRfIyKhwArgUiAPuFZE8voUuxmoN8ZMAf4X+KV/\no1RKjRaxkWEsnJrK2h1HGe5aRe32LnYdaQq6aiqw/o7jLKDUGHPAGNMBrAKW9imzFHja+fwl4GIZ\nSYsdK6WCypJZ6RxpbOPTw43DOs6Ow010dHUH1fiNHlYnjiygotd2pXOfyzLGGDvQCJw2P7OI3CIi\nW0RkS22t90d/KqVGh0W5YwkNEd4YZu+qnobxYBox3sPqxOHqzqHv/aE7ZTDGrDTGFBpjClNTU70S\nnFJq9EmMieCcScm8MczqquLyBrISo32+sqAVrE4clUB2r+1xwJH+yohIGJAAeKfLg1JKubB4VjoH\n6loorTkx5GMUlddTMD74qqnA+sSxGZgqIhNFJAK4BnilT5lXgBucz5cB75rhtloppdQALslLAxhy\n76qqxpNUNbYFZTUVWJw4nG0WtwFrgRLgRWPMThG5V0QudxZ7HEgWkVLgB8BpXXaVUsqb0mxRFOQk\nDnnSw+Ly4Frxry/L1xw3xqwB1vTZd3ev523A1f6OSyk1ui2Zlc7/rNlNxfFWssfEePTeokP1RIaF\nkBdkA/96WF1VpZRSI9LimenA0KqrisrrmZ2VQERYcP6JDc5/lVJKDdP45FhyM2weJ452exc7gnTg\nXw9NHEop1Y8lM9PZcqiemuY2t9+z60gTHfbuoJsRtzdNHEop1Y/Fs9IwBt7aVe32e4qcDePB2hUX\nNHEopVS/pqfFMyE5hrU73U8cxeX1ZCZEkRaEA/96aOJQSql+iAiLZ6WzsbSOxpOdbr2nuLyB/CC+\n2wBNHEopNaAlM9Oxdxve3T34XUd1UxuHG04GdfsGaOJQSqkBzR2XSLotyq1JD4N1xb++NHEopdQA\nQkKExTPTWLe3ltYO+4Bli8obiAgNYWZmcA7866GJQymlBrF4Vjptnd2s3zvwkg1Fh+qZlWUjMizU\nT5FZQxOHUkoN4qwJY0iKCR+wd1WHvZtPDzcG7fxUvWniUEqpQYSFhrAoN423S6rpsHe7LFNS1UR7\nkA/866GJQyml3LBkVjrNbXY+OnDM5eunVvwbH9wN46CJQyml3HLulBRiI0L77V1VVN5Aui2KjIRo\nP0fmf5o4lFLKDVHhoVw4Yyxv7TpKV/fpa8k5VvwL/rsN0MShlFJuWzIrnboTHWw9VP+5/TXNbVTW\nB//Avx6aOJRSyk1fmD6WiLCQ06qrPlvxT+84lFJK9RIXGcb5U1JYu/MoxnxWXVVUXk94qDAzM8HC\n6PxHE4dSSnlg8ax0DjecZOeRplP7issbmJmZQFR4cA/866GJQymlPLAoN43QEDlVXdXZ1c32yoZR\nU00FmjiUUsojY2IjOHviGN5wLim7u6qZts7RMfCvhyYOpZTy0JJZ6ZTWnKC0ppniip6Bf5o4lFJK\n9eOSvHQA1u6spuhQPWPjI8lMCN4V//qyLHGIyBgReUtE9jl/ukzXIvKGiDSIyKv+jlEppVxJT4hi\nXnYia3cepai8gYKcJETE6rD8xso7jruAd4wxU4F3nNuuPAh8y29RKaWUG5bMSmd7ZSPlx1tHzYjx\nHlYmjqXA087nTwNXuCpkjHkHaPZXUEop5Y7FM9NPPR8NU6n3ZmXiSDPGVAE4f461MBallPLIxJRY\nZqTHExYizM4aHQP/eoT58uAi8jaQ7uKln/jgXLcAtwDk5OR4+/BKKXWa7y+ayu6jzaNm4F8PnyYO\nY8yi/l4TkWoRyTDGVIlIBlAzzHOtBFYCFBYWnj51pVJKedmSWRksmZVhdRh+Z2VV1SvADc7nNwB/\ntzAWpZRSbrIycdwPfFFE9gFfdG4jIoUi8lhPIRH5APgLcLGIVIrIYkuiVUopBfi4qmogxphjwMUu\n9m8B/qnX9vn+jEsppdTAdOS4Ukopj2jiUEop5RFNHEoppTyiiUMppZRHNHEopZTyiPReNzdYiEgt\ncMjHp0kB6nx8Dm8KtHhBY/aXQIs50OKFwIl5vDEmdbBCQZk4/EFEthhjCq2Ow12BFi9ozP4SaDEH\nWrwQmDEPRKuqlFJKeUQTh1JKKY9o4hi6lVYH4KFAixc0Zn8JtJgDLV4IzJj7pW0cSimlPKJ3HEop\npTyiiaMfIpItIu+JSImI7BSRO1yU+YKINIrINufjbiti7RNTmYh86oxni4vXRUR+JyKlIrJdRAqs\niLNXPNN7Xb9tItIkIt/vU8by6ywiT4hIjYjs6LVvjIi8JSL7nD9drh8qIjc4y+wTkRtclfFjzA+K\nyG7n//3LIuJysezBPkd+jPceETnc6//+sn7eu0RE9jg/13f5I94BYn6hV7xlIrKtn/f6/Rp7jTFG\nHy4eQAZQ4HweD+wF8vqU+QLwqtWx9ompDEgZ4PXLgNcBAeYDH1sdc6/YQoGjOPqSj6jrDCwECoAd\nvfY9ANzlfH4X8EsX7xsDHHD+THI+T7Iw5kuAMOfzX7qK2Z3PkR/jvQf4oRufm/3AJCAC+KTv76o/\nY+7z+kPA3SPlGnvroXcc/TDGVBljipzPm4ESIMvaqLxiKfCMcdgEJDpXYBwJLgb2G2N8PXjTY8aY\n9cDxPruXAk87nz8NXOHirYuBt4wxx40x9cBbwBKfBdqLq5iNMW8aY+zOzU3AOH/E4o5+rrE7zgJK\njTEHjDEdwCoc/zc+N1DMIiLA14Dn/RGLP2nicIOITADygY9dvHyOiHwiIq+LyEy/BuaaAd4Uka3O\nddj7ygIqem1XMnIS4jX0/0s20q4zQJoxpgocXzSAsS7KjOTrvRzH3acrg32O/Ok2Z9XaE/1UB47U\na3w+UG2M2dfP6yPpGntEE8cgRCQOWA183xjT1OflIhzVKnOBh4G/+Ts+F841xhQAlwLfFZGFfV4X\nF++xvGudiEQAl+NY7bGvkXid3TVSr/dPADvw536KDPY58pc/AJOBeUAVjqqfvkbkNQauZeC7jZFy\njT2miWMAIhKOI2n82Rjz176vG2OajDEnnM/XAOEikuLnMPvGdMT5swZ4GcdtfG+VQHav7XHAEf9E\nN6BLgSJjTHXfF0bidXaq7qnmc/6scVFmxF1vZwP9l4FvGmdle19ufI78whhTbYzpMsZ0A4/2E8dI\nvMZhwJXAC/2VGSnXeCg0cfTDWT/5OFBijPl1P2XSneUQkbNwXM9j/ovytHhiRSS+5zmOhtAdfYq9\nAlzv7F01H2jsqW6xWL/fzkbade7lFaCnl9QNwN9dlFkLXCIiSc5qlkuc+ywhIkuAHwGXG2Na+ynj\nzufIL/q0v321nzg2A1NFZKLzzvUaHP83VloE7DbGVLp6cSRd4yGxunV+pD6A83Dc7m4HtjkflwG3\nArc6y9wG7MTRi2MTsMDimCc5Y/nEGddPnPt7xyzAChy9UD4FCkfAtY7BkQgSeu0bUdcZR1KrAjpx\nfMO9GUgG3gH2OX+OcZYtBB7r9d7lQKnzcZPFMZfiaA/o+Uw/4iybCawZ6HNkUbzPOj+n23Ekg4y+\n8Tq3L8PR83G/v+LtL2bn/qd6Pr+9ylp+jb310JHjSimlPKJVVUoppTyiiUMppZRHNHEopZTyiCYO\npZRSHtHEoZRSyiOaOJRSSnlEE4dSXiQiE3pPse3he28UkUxvx6SUt2niUGrkuBHHIDG3Oae2UMqv\nNHEo1YvzjqFERB4VxwJeb4pIdD9lp4jI285Ze4tEZHKf128Ukd/32n5VHItShYrIUyKyw7mQz7+K\nyDIcI87/7FzYJ1pEzhCRdc7ZU9f2mhfrfRH5HxFZB9whIlc7j/WJiKz34eVRCgD9tqLU6aYC1xpj\nvi0iLwJXAX9yUe7PwP3GmJdFJArHFzFXU6v3NQ/IMsbMAhCRRGNMg4jchmPRoi3OCTYfBpYaY2pF\n5OvAL3BMXwKQaIy5wPn+T4HFxpjD0s+Kfkp5kyYOpU530BjTs9znVmBC3wLOCeqyjDEvAxhj2pz7\n3Tn+AWCSiDwMvAa86aLMdGAW8JbzmKE45kTq0XvW1Q3AU84kd9oszkp5myYOpU7X3ut5F+Cqqsqd\nDGHn89XBUQDGmHoRmYtjdcDv4lglbnmf9wqw0xhzTj/Hbul5Yoy5VUTOBr4EbBORecaYkTB7sApS\n2sah1BAYx6JelSJyBYCIRIpITJ9iZcA8EQkRkWyc6y041xIJMcasBv4Tx5rVAM041rcH2AOkisg5\nzveE97fyoYhMNsZ8bIy5G6jj82tTKOV1eseh1NB9C/ijiNyLY1rtq4HuXq9vAA7imBZ8B46VDMGx\nrOmTItLzxe3Hzp9PAY+IyEngHGAZ8DsRScDxu/obHFNw9/WgiEzFcZfyDo6pupXyGZ1WXSmllEe0\nqkoppZRHtKpKqUGIyArg3D67f2uMedKKeJSymlZVKaWU8ohWVSmllPKIJg6llFIe0cShlFLKI5o4\nlFJKeUQTh1JKKY/8f0wMSvAPsWp2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b4a584b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#由上可以看出在取聚类数目取10时，效果最好因此在该附件继续挑接参数\n",
    "n_clusters_arrary = [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]\n",
    "MiniBatchKMeansByCH_scores(n_clusters_arrary,PE_event)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 由CH_scores可知该数据聚类数越小时聚类效果越好，此时聚类数目为２类，且轮廓系数大于0.5比较适合聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13418, 61)"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#尝试pca降维后聚类效果是否可以提升\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=0.85)\n",
    "pca.fit(PE_event)\n",
    "pca_pe_event = pca.transform(PE_event)\n",
    "pca_pe_event.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94.0231132777706 10 0.05178383730483805\n",
      "108.85115044605844 20 0.013832203691775605\n",
      "72.97695294986718 30 -0.039329554607627125\n",
      "63.27886073767731 40 -0.16335017077190456\n",
      "52.84242121874265 50 -0.144270424406846\n",
      "54.917213058800726 60 -0.013713715267973756\n",
      "60.421254910711035 70 -0.03880816710396365\n",
      "56.16360646402945 80 -0.03506255896547429\n",
      "64.08126526580558 90 -0.05122412167450526\n",
      "63.97693938378947 100 -0.10439016743673195\n"
     ]
    },
    {
     "data": {
      "image/png": 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CNNwkDVS6rYKL7n6LiDtP/r8xrXYIJBks+XAHP/3XImYsL6OwWwduOXcw4wpz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\n1/8HzF0wMeCZ8csAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b4a5a2c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DYM455lcFmDQsm+REfQQlNAb2TecX5xzHm2u388Cr/jWd9O0dbWZZZjbbzFZ5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\n/wEwV05I7zxf7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b4a3e9fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_clusters_arrary = [10,20,30,40,50,60,70,80,90,100]\n",
    "MiniBatchKMeansByCH_scores(n_clusters_arrary,pca_pe_event)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "122.22540343601376 2 0.39445131144352774\n",
      "120.2630150502303 3 0.11250657932241381\n",
      "92.60878616355481 4 0.37796520989359284\n",
      "108.91577571914806 5 0.0934448259938394\n",
      "73.19566612996346 6 0.3125153842252812\n",
      "67.36670299089013 7 0.12844488755747355\n",
      "108.36477843579618 8 0.11097115772259299\n",
      "105.90971037803604 9 0.1565730727207599\n",
      "111.1503330164354 10 0.11041838186968492\n",
      "95.34073836460202 11 -0.0005042959005806938\n",
      "100.97135083332999 12 0.04685054717643976\n",
      "68.43564050758681 13 0.0815087774971622\n",
      "85.83598266522813 14 0.14494408909077267\n",
      "83.63627471418948 15 0.01128698117060515\n",
      "102.0928995401925 16 0.10211211432861815\n",
      "92.60101044476109 17 0.07538831294702612\n",
      "77.86349430161427 18 -0.13270916999772026\n",
      "75.15992255749124 19 0.07310424224609126\n"
     ]
    },
    {
     "data": {
      "image/png": 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rn71qHm9WN7Nlf2PAnjca+RMkPgP0Ab/DXcOpB7gnGI2abkry3DWc+gZcbKlo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\nwOxht/OBY6Fp3ZjeBbxljKkfeUckvs4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b4e23ac10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Wh7bz/X9UMTjs4qbzllq+sNJuL+9r4fbn93F56UI+NsXEfcmqPO5/s46n32ni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\nOJRSSgXl/wH2nE1Pr1xfngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b4a47d790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#从新归一化新的数据\n",
    "pca_pe_event = preprocessing.normalize(pca_pe_event, norm='l2',axis=0,copy=False)\n",
    "pca_pe_event\n",
    "n_clusters_arrary = [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]\n",
    "MiniBatchKMeansByCH_scores(n_clusters_arrary,pca_pe_event)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 由CH_scores可知当对目标进行降维处理后，该数据聚类效果较随聚类数没有明显的变化，此时聚类数目为２类时轮廓系数较大，且聚类效果并没有比未做降维处理前有提升，因此选择MiniBatchKMeans(n_clusters = ２,init='k-means++')为最终模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R6vMoXVTBcQ0NSXzqPe8p/ZjFMoNDetOCw8FdaAqlEF/+srt7g/glZVWVTB8B4Ac/yH28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\nv+bK0JAE6DX1Vvt9jYzIc83u0gLE4FJEf/zHxQk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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b4a541550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "####做聚类示意图\n",
    "mb_kmeans = MiniBatchKMeans(n_clusters =2,init='k-means++')\n",
    "mb_kmeans.fit(PE_event)\n",
    "y_train_pred = mb_kmeans.labels_\n",
    "colors = ['b','r']\n",
    "pca_tr = pca.transform(PE_event)\n",
    "for i in range(2):\n",
    "    index = np.nonzero(y_train_pred==i)[0]\n",
    "    #选取有ＰＣＡ得到的主成分占比最大的两个特征分别为ｘ，ｙ轴\n",
    "    x1 = pca_tr[index,0]\n",
    "    x2 = pca_tr[index,1]\n",
    "    for j in range(len(x1)):\n",
    "        if j < 700:\n",
    "            plt.scatter(x1[j],x2[j],c=colors[i],marker=(2,1),alpha=0.8,lw=1,facecolors=\"none\",s=100)\n",
    "plt.axis([-0.03,0.1,-0.02,0.02])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
